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Updated: Sep 15, 2025
05:30
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
664
A Quantum-Classical Hybrid Model for Long-term Network Traffic Prediction
Yuhong Huang1, Yongmei Li2, Shuai Hou1
1China Mobile Research Institute.
Journal of Visualized Experiments : Jove
|July 14, 2025
Summary
A new Quantum TSMixer (QTSMixer) model enhances network traffic prediction by integrating quantum neural networks. This hybrid approach improves handling of periodic signals and long-term dependencies, outperforming existing methods.
Area of Science:
- Computer Science
- Artificial Intelligence
- Quantum Computing
Background:
- Network traffic prediction is vital for network management and optimization.
- Traditional methods and TSMixer show promise but struggle with periodic signals and long-term predictions.
Purpose of the Study:
- To introduce a novel Quantum TSMixer (QTSMixer) model for improved network traffic prediction.
- To leverage quantum neural networks for enhanced feature extraction in time series analysis.
Main Methods:
- Developed a hybrid quantum-classical model (QTSMixer) combining multi-layer perception and quantum neural networks.
- Incorporated trainable parameters to control quantum component influence.
- Empirically analyzed QTSMixer on real-world network traffic datasets.
Main Results:
- QTSMixer demonstrated superior performance compared to TSMixer.
- Achieved a 6.72% improvement in long-term network traffic prediction accuracy.
- Validated practical application capability and cross-domain potential.
Conclusions:
- QTSMixer effectively addresses limitations of TSMixer in network traffic prediction.
- The hybrid quantum-classical approach shows significant potential for time series analysis.
- Future research can extend QTSMixer to financial markets and weather prediction.
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